Supplementary Table 3 Morphological attributes of greenspace and adjacent reference built-up land. NDVI: normalized difference of vegetation index; IMP: impervious surface fraction; SVF: sky view factor. Values in parentheses are for built-up reference. 

City

ID

Type

NDVI

IMP (%)

Urban SVF

Greenspace image

Urban image

San Lorenzo, USA

1

Grass

0.32 (0.09)

19 (83)

0.7 - 1

San Lorenzo, USA

2

Tree-grass Mixture

0.37 (0.11)

10 (96)

0.7 - 1

San Lorenzo, USA

3

Grass

0.26 (0.11)

42 (96)

0.7 - 1

Los Angeles, USA

4

Grass

0.35 (0.09)

9 (86)

0.7 - 1

Los Angeles, USA

5

Grass

0.37 (0.12)

12 (89)

0.7 - 1

Los Angeles, USA

6

Grass

0.4 (0.12)

19 (89)

0.7 - 1

Los Angeles, USA

7

Grass

0.35 (0.12)

23 (86)

0.7 - 1

Los Angeles, USA

8

Grass

0.34 (0.10)

17 (89)

0.7 - 1

Los Angeles, USA

9

Grass

0.44 (0.08)

19 (99)

0.7 - 1

Los Angeles, USA

10

Grass

0.33 (0.09)

34 (99)

0.2 – 0.6

San Francisco, USA

11

Tree Canopy

0.38 (0.10)

10 (92)

0.2 – 0.6

San Francisco, USA

12

Grass

0.36 (0.10)

12 (92)

0.2 – 0.6

San Francisco, USA

13

Tree Canopy

0.35 (0.12)

8 (92)

0.2 – 0.6

San Francisco, USA

14

Tree-grass Mixture

0.2 (0.13)

21 (99)

0.7 - 1

Provo, USA

15

Grass

0.44 (0.15)

37 (96)

0.2 – 0.6

Provo, USA

16

Grass

0.49 (0.15)

27 (96)

0.2 – 0.6

Provo, USA

17

Grass

0.6 (0.16)

18 (96)

0.2 – 0.6

Logan, USA

18

Tree-grass Mixture

0.62 (0.19)

10 (98)

0.7 - 1

Logan, USA

19

Tree-grass Mixture

0.64 (0.15)

10 (92)

0.2 – 0.6

Logan, USA

20

Crop

0.51 (0.24)

6 (73)

0.6 – 0.9

Logan, USA

21

Tree Canopy

0.51 (0.14)

25 (100)

0.7 - 1

Logan, USA

22

Grass

0.41 (0.17)

21 (99)

0.7 - 1

Logan, USA

23

Grass

0.36 (0.17)

9 (99)

0.7 - 1

Logan, USA

24

Crop

0.5 (0.14)

12 (98)

0.7 - 1

Logan, USA

25

Crop

0.58 (0.16)

6 (99)

0.7 - 1

Logan, USA

26

Grass

0.59 (0.15)

14 (73)

0.7 - 1

Logan, USA

27

Tree-grass Mixture

0.6 (0.14)

31 (98)

0.7 - 1

Logan, USA

28

Crop

0.37 (0.13)

10 (98)

0.7 - 1

London, UK

29

Tree-grass Mixture

0.25 (0.16)

22 (96)

0.6 - 0.9

London, UK

30

Tree-grass Mixture

0.39 (0.04)

11 (100)

0.3 - 0.6

London, UK

31

Tree-grass Mixture

0.39 (0.08)

14 (99)

0.3 - 0.6

North Platte, USA

32

Crop

0.57 (0.16)

3 (91)

0.7 - 1

North Platte, USA

33

Crop

0.59 (0.34)

1 (57)

0.7 - 1

North Platte, USA

34

Grass

0.6 (0.25)

1 (60)

0.7 - 1

North Platte, USA

35

Crop

0.63 (0.31)

2 (60)

0.7 - 1

North Platte, USA

36

Tree-grass Mixture

0.6 (0.27)

3 (60)

0.7 - 1

North Platte, USA

37

Tree-grass Mixture

0.54 (0.27)

9 (60)

0.7 - 1

Anchorage, USA

38

Tree-grass Mixture

0.47 (0.17)

15 (66)

0.7 - 1

Anchorage, USA

39

Tree Canopy

0.26 (0.14)

9 (87)

0.7 - 1

Anchorage, USA

40

Tree Canopy

0.73 (0.13)

2 (78)

0.7 - 1

Anchorage, USA

41

Tree Canopy

0.44 (0.15)

14 (80)

0.7 - 1

Anchorage, USA

42

Tree-grass Mixture

0.58 (0.16)

9 (66)

0.7 - 1

Anchorage, USA

43

Tree-grass Mixture

0.41 (0.16)

9 (66)

0.7 - 1

Anchorage, USA

44

Tree Canopy

0.57 (0.13)

3 (78)

0.7 - 1

Anchorage, USA

45

Tree Canopy

0.5 (0.14)

12 (66)

0.7 - 1

Anchorage, USA

46

Tree Canopy

0.51 (0.13)

6 (78)

0.7 - 1

Washington DC, USA

47

Tree Canopy

0.74 (0.26)

5 (90)

0.5 - 0.8

Washington DC, USA

48

Tree Canopy

0.5 (0.23)

24 (90)

0.5 - 0.8

Washington DC, USA

49

Tree-grass Mixture

0.37 (0.23)

56 (90)

0.5 - 0.8

Washington DC, USA

50

Tree-grass Mixture

0.37 (0.23)

30 (90)

0.5 - 0.8

Washington DC, USA

51

Tree-grass Mixture

0.56 (0.24)

18 (90)

0.5 - 0.8

Washington DC, USA

52

Grass

0.5 (0.23)

20 (90)

0.5 - 0.8

Washington DC, USA

53

Tree Canopy

0.66 (0.23)

6 (90)

0.5 - 0.8

New Haven, USA

54

Tree Canopy

0.77 (0.34)

6 (93)

0.5 - 0.8

New Haven, USA

55

Tree Canopy

0.73 (0.34)

9 (94)

0.3 - 0.6

New Haven, USA

56

Tree-grass Mixture

0.56 (0.34)

24 (93)

0.5 - 0.8

New Haven, USA

57

Tree Canopy

0.54 (0.40)

7 (67)

0.6 - 0.9

New Haven, USA

58

Tree-grass Mixture

0.45 (0.28)

35 (93)

0.6 - 0.9

New Haven, USA

59

Grass

0.47 (0.25)

31 (93)

0.6 - 0.9

New Haven, USA

60

Tree Canopy

0.55 (0.24)

15 (93)

0.6 - 0.9

New Haven, USA

61

Tree Canopy

0.67 (0.41)

10 (67)

0.6 - 0.9

New Haven, USA

62

Tree Canopy

0.81 (0.24)

4 (85)

0.7 - 1

New Haven, USA

63

Tree-grass Mixture

0.62 (0.25)

14 (93)

0.6 - 0.9

New Haven, USA

64

Tree Canopy

0.85 (0.25)

3 (93)

0.6 - 0.9

New Haven, USA

65

Grass

0.42 (0.25)

22 (86)

0.7 - 1

New Haven, USA

66

Tree-grass Mixture

0.62 (0.34)

4 (93)

0.5 - 0.8

North Branford, USA

67

Grass

0.46 (0.16)

0 (84)

0.5 - 0.8

North Branford, USA

68

Tree Canopy

0.57 (0.16)

2 (84)

0.5 - 0.8

North Branford, USA

69

Crop

0.47 (0.16)

3 (84)

0.5 - 0.8

North Branford, USA

70

Tree Canopy

0.45 (0.16)

10 (84)

0.5 - 0.8

North Branford, USA

71

Tree-grass Mixture

0.48 (0.16)

10 (84)

0.5 - 0.8

Nanjing, China

72

Tree-grass Mixture

0.57 (0.35)

46 (77)

0.5 - 0.7

Nanjing, China

73

Crop

0.35 (0.33)

21 (88)

0.5 - 0.7

Nanjing, China

74

Tree Canopy

0.49 (0.23)

44 (93)

0.2 - 0.4

Nanjing, China

75

Tree-grass Mixture

0.53 (0.33)

39 (88)

0.5 - 0.7

Nanjing, China

76

Tree Canopy

0.74 (0.45)

4 (60)

0.5 - 0.8

Nanjing, China

77

Grass

0.51 (0.33)

38 (88)

0.5 - 0.7

Nanjing, China

78

Crop

0.46 (0.33)

36 (88)

0.5 - 0.7

Nanjing, China

79

Tree Canopy

0.62 (0.33)

16 (88)

0.5 - 0.7

Hangzhou, China

80

Tree Canopy

0.5 (0.26)

4 (57)

0.7 - 1

Hangzhou, China

81

Tree Canopy

0.40 (0.22)

4 (57)

0.7 - 1

Hangzhou, China

82

Crop

0.57 (0.30)

7 (57)

0.7 - 1

Hangzhou, China

83

Crop

0.58 (0.30)

15 (57)

0.7 - 1

Hangzhou, China

84

Crop

0.56 (0.30)

13 (57)

0.7 - 1

Hangzhou, China

85

Crop

0.46 (0.17)

29 (82)

0.5 - 0.7

Hangzhou, China

86

Tree Canopy

0.68 (0.27)

6 (81)

0.2 - 0.4

Hangzhou, China

87

Crop

0.35 (0.24)

12 (85)

0.5 - 0.8

Hangzhou, China

88

Tree Canopy

0.5 (0.24)

6 (85)

0.5 - 0.8

Hangzhou, China

89

Grass

0.4 (0.18)

35 (82)

0.5 - 0.7

Hangzhou, China

90

Grass

0.35 (0.20)

27 (81)

0.2 - 0.4

Hangzhou, China

91

Grass

0.41 (0.18)

45 (81)

0.2 - 0.4

Hangzhou, China

92

Tree Canopy

0.40 (0.19)

38 (83)

0.5 - 0.7

Hangzhou, China

93

Tree Canopy

0.41 (0.11)

42 (92)

0.3 - 0.6

Hangzhou, China

94

Grass

0.30 (0.19)

39 (94)

0.7 - 1

Hangzhou, China

95

Crop

0.32 (0.20)

31 (81)

0.2 - 0.4

Hangzhou, China

96

Grass

0.40 (0.19)

28 (81)

0.2 - 0.4

Hangzhou, China

97

Tree-grass Mixture

0.39 (0.19)

30 (81)

0.2 - 0.4

Hangzhou, China

98

Crop

0.37 (0.20)

31 (95)

0.5 - 0.7

Hong Kong, China

99

Tree Canopy

0.38 (0.08)

53 (99)

0.2 - 0.4

Hong Kong, China

100

Tree Canopy

0.24 (0.07)

55 (93)

0.2 - 0.4

Hong Kong, China

101

Tree Canopy

0.29 (0.08)

25 (99)

0.2 - 0.4

Hong Kong, China

102

Tree-grass Mixture

0.37 (0.06)

46 (99)

0.2 - 0.4

Guangzhou, China

103

Tree Canopy

0.57 (0.30)

29 (72)

0.5 - 0.7

Guangzhou, China

104

Tree Canopy

0.49 (0.40)

18 (71)

0.5 - 0.7

Guangzhou, China

105

Tree Canopy

0.40 (0.13)

16 (100)

0.6 - 0.9

Guangzhou, China

106

Tree Canopy

0.35 (0.14)

38 (100)

0.6 - 0.9

Guangzhou, China

107

Tree Canopy

0.32 (0.13)

35 (100)

0.6 - 0.9

Guangzhou, China

108

Tree Canopy

0.34 (0.16)

27 (99)

0.6 - 0.9

Guangzhou, China

109

Tree-grass Mixture

0.42 (0.29)

50 (90)

0.5 - 0.7

Guangzhou, China

110

Tree Canopy

0.47 (0.18)

19 (99)

0.6 - 0.9

Guangzhou, China

111

Crop

0.37 (0.29)

34 (83)

0.2 - 0.4

Guangzhou, China

112

Tree Canopy

0.56 (0.29)

19 (83)

0.2 - 0.4

Guangzhou, China

113

Tree-grass Mixture

0.34 (0.29)

25 (83)

0.2 - 0.4

Guangzhou, China

114

Tree-grass Mixture

0.47 (0.29)

8 (83)

0.2 - 0.4

 

Vegetation type is based on visual inspection of Google Earth images. NDVI is warm season value based on Sentinel-2 satellite observations. Cropland was identified using FROM-GLC10 global landcover data 2017 (https://data-starcloud.pcl.ac.cn/resource/1). IMP is based on ESA Worldwide Land Cover Mapping 2021 (https://esa-worldcover.org/en). SVF is the value of the dominant local climate zone (LCZ) of the built-up neighborhood. LCZ classification data: https://www.wudapt.org. SVF values associated with LCZs from Stewart (2012, ref46).